一种混合适应动态编程,用于优化USV的跟踪控制
概括
本研究介绍了一种混合适应动态编程 (ADP) 方法,用于无人驾驶地面车辆 (USV) 的跟踪控制. 与静态方法相比,动态事件驱动 (DED) 方法显著减少了数据需求,并提高了效率.
科学领域:
- 机器人和控制系统 机器人和控制系统
- 人工智能的人工智能
- 海洋工程 海洋工程
背景情况:
- 优化跟踪控制对于无人地面车辆 (USV) 导航至关重要.
- 现有的方法往往需要大量的数据或模型依赖.
- 适应动态编程 (ADP) 为复杂的控制问题提供了数据驱动的解决方案.
研究的目的:
- 开发一种高效的混合适应动态编程 (ADP) 方法,用于USV最佳跟踪控制.
- 整合综合强化学习 (IRL) 和动态事件驱动 (DED) 机制.
- 为了减少USV控制中的模型依赖性和网络传输负担.
主要方法:
- 为USV和参考轨迹建立了一个增强系统模型.
- 追踪Hamilton-Jacobi-Bellman (HJB) 方程,使用IRL推导.
- 动态事件驱动 (DED) 控制器更新规则和体验重复实现了ADP.
主要成果:
- 与静态事件驱动 (SED) 相比,提出的DED方法显著减少了78%的样本大小.
- 更新之间的平均间隔在DED方法中增加了大约四倍.
- 实现了对前和反控制组件的有效获取.
结论:
- 混合ADP方法与IRL和DED对于USV跟踪控制是有效的.
- DED机制提高了数据效率,并降低了计算负载.
- 这种方法为数据驱动的USV控制提供了有希望的解决方案,减少了模型依赖.
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